The new constructs in aspect-oriented programming can cause hazards to quality assurance. To detect aspect defects, this book presents a state-based approach to systematic modeling and testing of aspects. We extend finite state machines with notations for specifying incremental modification aspects, integration aspects, and combination aspects. We compose aspect models with their base class models through an explicit weaving mechanism. We adapt the round-trip path testing of object-oriented programs to the generation of test sequences from woven-state models. An incremental testing process is exploited to determine whether failures, if revealed, have to do with aspects. We have conducted a series of experiments based on three applications. To further evaluate the fault-detection ability, we have created a significant number of mutants (or variation versions) by seeding one or more aspect variations (i.e., potential faults) and exploited multi-variation mutants to investigate the effect of interaction between aspect variations. Our experiments indicate that our model-based testing approach is highly effective in the detection of the seeded aspect faults.
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Weifeng Xu, Ph.D.: Director of Keystone Software Development Institute & Assistant Professor at Gannon University, PA, USA.
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Taschenbuch. Condition: Neu. Neuware - The new constructs in aspect-oriented programmingcan cause hazards to quality assurance. To detectaspect defects, this book presents a state-basedapproach to systematic modeling and testing ofaspects. We extend finite state machines withnotations for specifying incremental modificationaspects, integration aspects, and combinationaspects. We compose aspect models with their baseclass models through an explicit weaving mechanism.We adapt the round-trip path testing of object-oriented programs to the generation of testsequences from woven-state models. An incrementaltesting process is exploited to determine whetherfailures, if revealed, have to do with aspects. Wehave conducted a series of experiments based onthree applications. To further evaluate the fault-detection ability, we have created a significantnumber of mutants (or variation versions) by seedingone or more aspect variations (i.e., potentialfaults) and exploited multi-variation mutants toinvestigate the effect of interaction between aspectvariations. Our experiments indicate that our model-based testing approach is highly effective in thedetection of the seeded aspect faults. Seller Inventory # 9783639075595
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